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Advanced Artificial Intelligence in Medicine and Bioinformatics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 5413

Special Issue Editors


E-Mail Website
Guest Editor
School of Computer Science and Technology, Hainan University, Haikou 570228, China
Interests: big data; machine learning; smart medicine

E-Mail Website
Guest Editor
School of Computer and Information and Key Laboratory of Knowledge Engineering with Big Data, Hefei University of Technology, Hefei 230002, China
Interests: human–computer interaction; wireless sensing; wireless communication; machine learning

Special Issue Information

Dear Colleagues,

With the development of deep learning, deep reinforcement learning, and few-shot learning, artificial intelligence technologies have made great progress. Some advanced artificial intelligence technologies have widely been applied in medicine and bioinformatics, resulting in the emergence of smart medicine. Smart medicine aims to provide precise personalized medical services such as accurate diagnosis and the optimal treatment policy. To achieve this goal, many advanced artificial intelligence techniques like machine learning, robotics, speech recognition, and natural language processing, together with cloud computing and privacy computing, should be proposed to analyze and process medical data and bioinformatics data for diagnostics and treatment.

This Special Issue aims to collect the ongoing research activities and clinical applications of advanced artificial intelligence technologies in the fields of medicine and bioinformatics. Topics of interest include, but are not limited to:

  • AI-based medical/bioinformatics big data analysis models and algorithms;
  • Privacy-aware medical/bioinformatics data analysis;
  • Federated learning for medical/bioinformatics data;
  • Natural language processing and knowledge discovery in biomedical data;
  • Edge and cloud computing for digital healthcare;
  • Security, trust, blockchain, and privacy in digital healthcare.

Prof. Dr. Qingchen Zhang
Dr. Jinyang Huang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep learning
  • few-shot learning
  • deep reinforcement learning
  • medical big data
  • bioinformtics big data
  • edge and cloud computing
  • privacy computing

Published Papers (3 papers)

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Research

13 pages, 2762 KiB  
Article
RMAU-Net: Breast Tumor Segmentation Network Based on Residual Depthwise Separable Convolution and Multiscale Channel Attention Gates
by Sheng Yuan, Zhao Qiu, Peipei Li and Yuqi Hong
Appl. Sci. 2023, 13(20), 11362; https://doi.org/10.3390/app132011362 - 16 Oct 2023
Viewed by 1099
Abstract
Breast cancer is one of the most common female diseases, posing a great threat to women’s health, and breast ultrasound imaging is a common method for breast cancer diagnosis. In recent years, U-Net and its variants have dominated the medical image segmentation field [...] Read more.
Breast cancer is one of the most common female diseases, posing a great threat to women’s health, and breast ultrasound imaging is a common method for breast cancer diagnosis. In recent years, U-Net and its variants have dominated the medical image segmentation field with their excellent performance. However, the existing U-type segmentation networks have the following problems: (1) the design of the feature extractor is complicated, and the calculation difficulty is increased; (2) the skip connection operation simply combines the features of the encoder and the decoder, without considering both spatial and channel dimensions; (3) during the downsampling phase, the pooling operation results in the loss of feature information. To address the above deficiencies, this paper proposes a breast tumor segmentation network, RMAU-Net, that combines residual depthwise separable convolution and a multi-scale channel attention gate. Specifically, we designed the RDw block, which has a simple structure and a larger sensory field, to overcome the localization problem of convolutional operations. Meanwhile, the MCAG module is designed to correct the low-level features in both spatial and channel dimensions and assist the high-level features to recover the up-sampling and pinpoint non-regular breast tumor features. In addition, this paper used the Patch Merging operation instead of the pooling method to prevent the loss of breast ultrasound image information. Experiments were conducted on two breast ultrasound datasets, Dataset B and BUSI, and the results show that the method in this paper has superior segmentation performance and better generalization. Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence in Medicine and Bioinformatics)
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19 pages, 1309 KiB  
Article
Generative Adversarial Networks in Retinal Image Classification
by Francesco Mercaldo, Luca Brunese, Fabio Martinelli, Antonella Santone and Mario Cesarelli
Appl. Sci. 2023, 13(18), 10433; https://doi.org/10.3390/app131810433 - 18 Sep 2023
Viewed by 1344
Abstract
The recent introduction of generative adversarial networks has demonstrated remarkable capabilities in generating images that are nearly indistinguishable from real ones. Consequently, both the academic and industrial communities have raised concerns about the challenge of differentiating between fake and real images. This issue [...] Read more.
The recent introduction of generative adversarial networks has demonstrated remarkable capabilities in generating images that are nearly indistinguishable from real ones. Consequently, both the academic and industrial communities have raised concerns about the challenge of differentiating between fake and real images. This issue holds significant importance, as images play a vital role in various domains, including image recognition and bioimaging classification in the biomedical field. In this paper, we present a method to assess the distinguishability of bioimages generated by a generative adversarial network, specifically using a dataset of retina images. Once the images are generated, we train several supervised machine learning models to determine whether these classifiers can effectively discriminate between real and fake retina images. Our experiments utilize a deep convolutional generative adversarial network, a type of generative adversarial network, and demonstrate that the generated images, although visually imperceptible as fakes, are correctly identified by a classifier with an F-Measure greater than 0.95. While the majority of the generated images are accurately recognized as fake, a few of them are not classified as such and are consequently considered real retina images. Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence in Medicine and Bioinformatics)
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20 pages, 3740 KiB  
Article
Variational Autoencoders for Data Augmentation in Clinical Studies
by Dimitris Papadopoulos and Vangelis D. Karalis
Appl. Sci. 2023, 13(15), 8793; https://doi.org/10.3390/app13158793 - 30 Jul 2023
Cited by 7 | Viewed by 2345
Abstract
Sample size estimation is critical in clinical trials. A sample of adequate size can provide insights into a given population, but the collection of substantial amounts of data is costly and time-intensive. The aim of this study was to introduce a novel data [...] Read more.
Sample size estimation is critical in clinical trials. A sample of adequate size can provide insights into a given population, but the collection of substantial amounts of data is costly and time-intensive. The aim of this study was to introduce a novel data augmentation approach in the field of clinical trials by employing variational autoencoders (VAEs). Several forms of VAEs were developed and used for the generation of virtual subjects. Various types of VAEs were explored and employed in the production of virtual individuals, and several different scenarios were investigated. The VAE-generated data exhibited similar performance to the original data, even in cases where a small proportion of them (e.g., 30–40%) was used for the reconstruction of the generated data. Additionally, the generated data showed even higher statistical power than the original data in cases of high variability. This represents an additional advantage for the use of VAEs in situations of high variability, as they can act as noise reduction. The application of VAEs in clinical trials can be a useful tool for decreasing the required sample size and, consequently, reducing the costs and time involved. Furthermore, it aligns with ethical concerns surrounding human participation in trials. Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence in Medicine and Bioinformatics)
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